The present disclosure relates generally to life estimation, and particularly to equipment subsystem life estimation.
Estimating a remaining useful life, also herein referred to as a remaining life, of a subsystem is known in the art as prognostics. Remaining life estimates provide valuable information for operation of modern complex equipment. Remaining life estimates provide decision making aids that allow operators to change operational characteristics (such as load), which in turn may prolong a life of the subsystem. Remaining life estimates also allow planners to account for upcoming maintenance and set in motion a logistics process that supports a smooth transition from faulted to fully functioning equipment. Predicting remaining life is not straightforward because, ordinarily, remaining life is dependent upon future usage conditions, such as load and speed, for example. In addition, an understanding of the underlying physics that govern remaining life is hard to come by in particular for complex machinery where numerous fault modes can potentially be the driver for remaining life. Examples of equipment that may benefit from use of remaining life estimates are aircraft engines (both military and commercial), medical equipment, and power plants.
A common approach to prognostics is to employ a model of damage propagation contingent on future use. Such a model is often times based on detailed materials knowledge and makes use of finite element modeling. Because such models are extremely costly to develop, they are limited to a few important parts of a subsystem, but are rarely applied to a full subsystem.
Another approach is a data-driven approach to take advantage of time series data where equipment behavior has been tracked via sensor measurements during normal operation all the way to an end of equipment useful life. The end of equipment useful life can represent a totally non-functioning state of the equipment for example, equipment failure. The end of equipment useful life can also represent a state of the equipment wherein the equipment no longer provides expected results. When a reasonably-sized set of these observations exists, pattern recognition algorithms can be employed to recognize these trends and predict remaining life. These predictions are often made under an assumption of near-constant future load conditions. However, such run to end of equipment useful life data are often not available because, when an observed system is complex, expensive, and, safety is important, such as aircraft engines, for example, faults will be repaired before they lead to the end of equipment useful life. This deprives the data-driven approach from information necessary for its proper application.
Accordingly, there is a need in the art for a life estimation arrangement that overcomes these limitations.